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Dual-Rate Adaptive Optimal Tracking Control for Dense Medium Separation Process Using Neural Networks.

Wei Dai, Lingzhi Zhang, Jun Fu

    IEEE Transactions on Neural Networks and Learning Systems
    |September 4, 2020
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    Summary
    This summary is machine-generated.

    This study introduces a novel dual-rate adaptive optimal tracking control for dense medium separation (DMS) coal cleaning. The approach effectively manages the multitime scale control challenges in DMS systems.

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    Area of Science:

    • Chemical Engineering
    • Control Systems
    • Materials Science

    Background:

    • Dense medium separation (DMS) is crucial for coal cleaning.
    • DMS control systems face challenges due to time-varying and nonlinear characteristics.
    • Existing control systems struggle with multitime scale operations.

    Purpose of the Study:

    • To propose a dual-rate adaptive optimal tracking control approach for DMS systems.
    • To address the inherent time-varying and nonlinear characteristics of DMS processes.
    • To improve the efficiency and stability of coal cleaning operations.

    Main Methods:

    • A nonlinear adaptive PI controller with a neural network (NN)-based compensator for the basic loop.
    • A lifting technique to unify multitime scale loops and formulate a generalized controlled object.
    • A data-driven operation optimization control using adaptive dynamic programming and reference control implemented with NNs.

    Main Results:

    • The proposed method effectively handles unknown dynamics in the generalized controlled object.
    • Simulation results demonstrate the effectiveness of the dual-rate adaptive optimal tracking control.
    • The approach ensures stability for the complex DMS control system.

    Conclusions:

    • The developed dual-rate adaptive optimal tracking control is effective for DMS coal cleaning.
    • The NN-based approach successfully manages multitime scale and nonlinear dynamics.
    • This method offers a robust solution for optimizing DMS process control.